Performance assessment of rotary drilling using non-linear multiple regression analysis and multilayer perceptron neural network View Full Text


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Article Info

DATE

2019-04

AUTHORS

Mohammad Darbor, Lohrasb Faramarzi, Mostafa Sharifzadeh

ABSTRACT

Cost and efficiency estimation for rotary drilling rigs is an essential step in the design of excavation projects. Due to the complexity of influencing factors on rotary drilling, sophisticated modeling methods are required for performance prediction. In this study, rate of penetration (ROP) of a rotary drilling machine using two developed modeling techniques, namely, non-linear multiple regression models (NLMR) and multilayer perceptron–artificial neural networks (MLP-ANN) were assessed. For this purpose, field and experimental data of various case studies were used. Several performance indexes, including determination coefficient (R2), variance accounted for (VAF), and root mean square error (RMSE), were evaluated to check the prediction capacity of the developed models. Considering multiple inputs in various NLMR models, the most influencing factors on ROP were determined to be brittleness, rock quality designation (RQD) index, water content, and anisotropy index. Multivariate analysis results of developed models showed that the MLP–ANN model indicates higher precision in performance prediction than the NLMR model for both the training and testing datasets. Additionally, sensitivity analysis showed that RQD and water content have significant influence on the ROP. The models proposed in this study can successfully be applied to predict the ROP in rocks with similar characteristics. More... »

PAGES

1501-1513

References to SciGraph publications

  • 2014-11. Real-Time Prediction and Optimization of Drilling Performance Based on a New Mechanical Specific Energy Model in ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
  • 2010-05. Assessment of some brittleness indexes in rock-drilling efficiency in ROCK MECHANICS AND ROCK ENGINEERING
  • 2015-06. Prediction of TBM penetration rate using intact and mass rock properties (case study: Zagros long tunnel, Iran) in ARABIAN JOURNAL OF GEOSCIENCES
  • 2004-09. Evaluation of drill cuttings in prediction of penetration rate by using coarseness index and mean particle size in percussive drilling in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2006-04. A Comparative Analysis of Cognitive Systems for the Prediction of Drillability of Rocks and Wear Factor in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2016-04. Cuttability Assessment of Selected Rocks Through Different Brittleness Values in ROCK MECHANICS AND ROCK ENGINEERING
  • 2017-01. Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models in ENGINEERING WITH COMPUTERS
  • 2013-10. Development of a New Index to Assess the Rock Mass Drillability in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2016-01. Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation in ENGINEERING WITH COMPUTERS
  • 1997-08. Drillability prediction: geological influences in hard rock drill and blast tunnelling in INTERNATIONAL JOURNAL OF EARTH SCIENCES
  • 2015-11. Cuttability assessment using the Drilling Rate Index (DRI) in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2017-02. Relationships between the drilling rate index and physicomechanical rock properties in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2012-06. An intelligent approach to evaluate drilling performance in NEURAL COMPUTING AND APPLICATIONS
  • 2016-02. Performance Evaluation of Button Bits in Coal Measure Rocks by Using Multiple Regression Analyses in ROCK MECHANICS AND ROCK ENGINEERING
  • 2016-08. Rock Drilling Performance Evaluation by an Energy Dissipation Based Rock Brittleness Index in ROCK MECHANICS AND ROCK ENGINEERING
  • 2016-04. Estimating the Penetration Rate in Diamond Drilling in Laboratory Works Using the Regression and Artificial Neural Network Analysis in NEURAL PROCESSING LETTERS
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    http://scigraph.springernature.com/pub.10.1007/s10064-017-1192-3

    DOI

    http://dx.doi.org/10.1007/s10064-017-1192-3

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